Integrated microscopy systems have contributed to the rapid growth of scientific breakthroughs in many disciplines, and particularly in the biosciences. Many such systems have associated image acquisition, processing, and analysis capabilities. For each microscopy experiment, a system can produce images and measurement data in multiple image-collection dimensions. For example, in addition to the lateral dimensions (e.g., the X-Y dimensions in the plane of the sample or object being viewed), images can be acquired at multiple focus positions (e.g., in the Z, or Focus Position, dimension), at multiple wavelengths using different fluorochromes or microscopy techniques (e.g., in the Wavelength or Channel dimension), during a time lapse (e.g., in the Time dimension), and/or over multiple areas in a sample (e.g., in the Stage dimension), during a multi-dimensional experiment. Numeric measurement data can also be derived from the images.
Visualization of the images and measurement data of a multi-dimensional experiment e.g., a set of measurements on a biological sample, allows a user to identify, assess, and compare features within the sample, under different experimental conditions, at different times, and in different areas within the sample.
Various visualization techniques have been developed to better facilitate the understanding and analysis of such sets of images and measurement data. For example, fluorescent images taken at different wavelengths tend to highlight different structures in a biological sample; these images can be aligned in a stack to produce a composite image to show the set of features and structures in the sample and their spatial relationships. By way of another example, images taken at different times can show the evolution of the sample over time; these images can be played in sequence, as in a movie, to allow the user to quickly visualize the changes in the sample over time. Furthermore, images taken along different spatial dimensions (e.g., the X-Y, and Z dimensions) can be combined and rendered as a 3D reconstruction, projected onto a particular dimension, or stitched together to form a cross-sectional view of the entire sample.
To further improve the image viewing quality, many currently available systems also have built-in software components that can apply various image processing techniques to the image data to reduce noise, sharpen focus, enhance certain regions of interest, create topographical surface maps of the image, and carry out other analyses.
In addition to viewing the image data, various computational and image processing techniques can be used, within currently available systems, to identify and characterize objects and structures in the acquired images. Numeric measurement data can be obtained from the images for the identified objects and structures and includes, for example, cell cycle measurements, cell or nuclei scoring, colocalization and brightness measurements, and movement data for identified objects. The measurement data for different images can be correlated with one another, or with values in one or more image-collection dimensions, and presented in graphs or tables along with the images.
However, currently available systems can capture images at a very high rate. A typical multi-dimensional experiment can easily produce tens of thousands of images available for viewing in a matter of minutes. A vast amount of information is embedded in the images and measurement data obtained from each multi-dimensional experiment. Understanding how any given image relates to another image is challenging. Extracting meaningful relationships and useful information out of the images and measurements are more challenging.
To interpret and understand the image and measurement data, the user often has to make comparisons between related images of different time points, different locations, different samples, or different experiments to identify and assess the commonality or differences in the related images. Although the currently available systems are capable of presenting images and numeric measurement data in graphs and tables along with the images, typically the selection and mode of presentation of images and measurement data must be decided by the user.
For example, if a user wishes to do a side-by-side comparison of two images containing a common object of interest, the user typically has to manually select the images from a database, place them side by side in an image viewer, and manipulate each image individually to locate the region of interest in the image in order to finally view the images side by side for proper comparison. When the user needs to examine the numeric data for the same images, the user also has to locate the correct file for numeric data associated with the images. This process can become very time consuming and tedious if the user has to compare a large number of images. Sometimes, the task becomes virtually impossible, because the user may not always be able to remember or even be aware of which images show the same object of interest, and therefore are not able to locate the appropriate images for visual comparison.
Furthermore, although images and data in multiple dimensions are available to the user, the user has to be familiar with the organization of the images in store in order to locate the images that he or she wishes to view. Navigating the tens of thousands of images in a typical multi-dimensional experiment is difficult without the help of some effective navigation tools. Even if the user makes an attempt to keep track of the relationships between the images and data (e.g., through use of descriptive filenames and folder hierarchies), this process is often error-prone. Errors in matching the images with the correct measurement data are often difficult to detect because the images in different datasets tend to be very similar in appearance. Important discoveries can be missed and gross misunderstandings can be caused by incorrect manual correlation of images by the user.
Therefore, there is a need for an integrated image visualization and manipulation interface that is fast and efficient, and simplifies the process for image navigation, review, and comparison of related images and measurement data from multi-dimensional experiments.